Fully automatic segmentation of the mitral leaflets in 3D transesophageal echocardiographic images using multi-atlas joint label fusion and deformable medial modeling |
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| Institution: | 1. Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, United States;2. Gorman Cardiovascular Research Group, University of Pennsylvania, Philadelphia, PA, United States;3. Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States;4. Department of Surgery, University of Pennsylvania, Philadelphia, PA, United States;1. Cancer Institute/Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing Key Laboratory of Clinical Study on Anticancer Molecular Targeted Drugs, Beijing, China;2. Center for Infectious Diseases, University of Texas School of Public Health, Houston, TX, USA;3. St. Luke''s Episcopal Hospital, Houston, TX, USA;4. Baylor College of Medicine, Houston, TX, USA;1. Shandong University School of Public Health, Jinan, China;2. National Centre for AIDS/STD Control and Prevention, Chinese Centre for Disease Control and Prevention, Beijing, China;3. Lanzhou Municipal Centre for Disease Control and Prevention, Lanzhou, China;4. Anshan Municipal Centre for Disease Control and Prevention, Anshan, China;5. Jilin Municipal Centre for Disease Control and Prevention, Jilin, China;6. Shandong University Centre for Health Management and Policy, Jinan, China;7. University of California, Los Angeles, School of Public Health, Los Angeles, CA, USA;1. Gorman Cardiovascular Research Group University of Pennsylvania, Philadelphia, Pennsylvania;2. Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania;3. Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania;4. School of Design, University of Pennsylvania, Philadelphia, Pennsylvania;5. Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania;1. Department of Clinic Laboratory, The Third Xiangya Hospital of Central South University, Changsha, Hunan, 410013, China |
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| Abstract: | Comprehensive visual and quantitative analysis of in vivo human mitral valve morphology is central to the diagnosis and surgical treatment of mitral valve disease. Real-time 3D transesophageal echocardiography (3D TEE) is a practical, highly informative imaging modality for examining the mitral valve in a clinical setting. To facilitate visual and quantitative 3D TEE image analysis, we describe a fully automated method for segmenting the mitral leaflets in 3D TEE image data. The algorithm integrates complementary probabilistic segmentation and shape modeling techniques (multi-atlas joint label fusion and deformable modeling with continuous medial representation) to automatically generate 3D geometric models of the mitral leaflets from 3D TEE image data. These models are unique in that they establish a shape-based coordinate system on the valves of different subjects and represent the leaflets volumetrically, as structures with locally varying thickness. In this work, expert image analysis is the gold standard for evaluating automatic segmentation. Without any user interaction, we demonstrate that the automatic segmentation method accurately captures patient-specific leaflet geometry at both systole and diastole in 3D TEE data acquired from a mixed population of subjects with normal valve morphology and mitral valve disease. |
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| Keywords: | Mitral valve 3D echocardiography Medial representation Multi-atlas segmentation Label fusion |
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